Relatively-long scan time remains as the major drawback of magnetic resonance imaging (MRI), compared to other medical imaging modalities like ultrasound and computed tomography. To reduce scan time, in accelerated MRI, significantly fewer magnetic resonance imaging measurements than traditionally though necessary are acquired. In reconstructing images acquired with accelerated MRI, promoting sparsity during image reconstruction—such as compressed sensing—has emerged as a powerful technique for improving signal-to-noise ratio and suppressing undersampling artifact. However, sparse reconstruction of non-Cartesian MRI data remains computationally challenging because multiple “gridding” operations must be executed at each iteration of the reconstruction.
It would be desirable to have a system and method for efficiently generating MR images in accelerated MRI while still promoting the sparsity in acquisition.